| Title: | Leveraging fine-mapping and multipopulation training data to improve cross-population polygenic risk scores |
| Journal: | Nature Genetics |
| Published: | 7 Apr 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/35393596/ |
| DOI: | https://doi.org/10.1038/s41588-022-01036-9 |
| URL: | https://www.medrxiv.org/content/medrxiv/early/2021/08/20/2021.01.19.21249483.full.pdf |
Publication 7157
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Abstract
Polygenic risk scores suffer reduced accuracy in non-European populations, exacerbating health disparities. We propose PolyPred, a method that improves cross-population polygenic risk scores by combining two predictors: a new predictor that leverages functionally informed fine-mapping to estimate causal effects (instead of tagging effects), addressing linkage disequilibrium differences, and BOLT-LMM, a published predictor. When a large training sample is available in the non-European target population, we propose PolyPred+, which further incorporates the non-European training data. We applied PolyPred to 49 diseases/traits in four UK Biobank populations using UK Biobank British training data, and observed relative improvements versus BOLT-LMM ranging from +7% in south Asians to +32% in Africans, consistent with simulations. We applied PolyPred+ to 23 diseases/traits in UK Biobank east Asians using both UK Biobank British and Biobank Japan training data, and observed improvements of +24% versus BOLT-LMM and +12% versus PolyPred. Summary statistics-based analogs of PolyPred and PolyPred+ attained similar improvements.10 Authors
- Omer Weissbrod
- Masahiro Kanai
- Huwenbo Shi
- Steven Gazal
- Wouter J. Peyrot
- Amit V. Khera
- Yukinori Okada
- Alicia R. Martin
- Hilary K. Finucane
- Alkes L. Price
Enabling scientific discoveries that improve human health